| import time | |
| import torch | |
| from hpc_rll.origin.td import td_lambda_error, td_lambda_data | |
| from hpc_rll.rl_utils.td import TDLambda | |
| from testbase import mean_relative_error, times | |
| assert torch.cuda.is_available() | |
| use_cuda = True | |
| T = 1024 | |
| B = 64 | |
| def td_val(): | |
| ori_value = torch.randn(T + 1, B) | |
| ori_reward = torch.randn(T, B) | |
| ori_weight = torch.randn(T, B) | |
| hpc_value = ori_value.clone().detach() | |
| hpc_reward = ori_reward.clone().detach() | |
| hpc_weight = ori_weight.clone().detach() | |
| hpc_td = TDLambda(T, B) | |
| if use_cuda: | |
| ori_value = ori_value.cuda() | |
| ori_reward = ori_reward.cuda() | |
| ori_weight = ori_weight.cuda() | |
| hpc_value = hpc_value.cuda() | |
| hpc_reward = hpc_reward.cuda() | |
| hpc_weight = hpc_weight.cuda() | |
| hpc_td = hpc_td.cuda() | |
| ori_value.requires_grad_(True) | |
| ori_loss = td_lambda_error(td_lambda_data(ori_value, ori_reward, ori_weight)) | |
| ori_loss = ori_loss.mean() | |
| ori_loss.backward() | |
| if use_cuda: | |
| torch.cuda.synchronize() | |
| hpc_value.requires_grad_(True) | |
| hpc_loss = hpc_td(hpc_value, hpc_reward, hpc_weight) | |
| hpc_loss = hpc_loss.mean() | |
| hpc_loss.backward() | |
| if use_cuda: | |
| torch.cuda.synchronize() | |
| mre = mean_relative_error( | |
| torch.flatten(ori_loss).cpu().detach().numpy(), | |
| torch.flatten(hpc_loss).cpu().detach().numpy() | |
| ) | |
| print("td fp mean_relative_error: " + str(mre)) | |
| mre = mean_relative_error( | |
| torch.flatten(ori_value.grad).cpu().detach().numpy(), | |
| torch.flatten(hpc_value.grad).cpu().detach().numpy() | |
| ) | |
| print("td bp mean_relative_error: " + str(mre)) | |
| def td_perf(): | |
| ori_value = torch.randn(T + 1, B) | |
| ori_reward = torch.randn(T, B) | |
| ori_weight = torch.randn(T, B) | |
| hpc_value = ori_value.clone().detach() | |
| hpc_reward = ori_reward.clone().detach() | |
| hpc_weight = ori_weight.clone().detach() | |
| hpc_td = TDLambda(T, B) | |
| if use_cuda: | |
| ori_value = ori_value.cuda() | |
| ori_reward = ori_reward.cuda() | |
| ori_weight = ori_weight.cuda() | |
| hpc_value = hpc_value.cuda() | |
| hpc_reward = hpc_reward.cuda() | |
| hpc_weight = hpc_weight.cuda() | |
| hpc_td = hpc_td.cuda() | |
| ori_value.requires_grad_(True) | |
| for i in range(times): | |
| t = time.time() | |
| ori_loss = td_lambda_error(td_lambda_data(ori_value, ori_reward, ori_weight)) | |
| ori_loss = ori_loss.mean() | |
| ori_loss.backward() | |
| if use_cuda: | |
| torch.cuda.synchronize() | |
| print('epoch: {}, original td cost time: {}'.format(i, time.time() - t)) | |
| hpc_value.requires_grad_(True) | |
| for i in range(times): | |
| t = time.time() | |
| hpc_loss = hpc_td(hpc_value, hpc_reward, hpc_weight) | |
| hpc_loss = hpc_loss.mean() | |
| hpc_loss.backward() | |
| if use_cuda: | |
| torch.cuda.synchronize() | |
| print('epoch: {}, hpc td cost time: {}'.format(i, time.time() - t)) | |
| if __name__ == '__main__': | |
| print("target problem: T = {}, B = {}".format(T, B)) | |
| print("================run td validation test================") | |
| td_val() | |
| print("================run td performance test================") | |
| td_perf() | |